where the proposed loss function resembles a regular supervised setup with the specified signal-level loss function \({\cal L}\) . Our method does not artificially alter the input SNR distributions similar to MixIT-like [1]}, [2]}, [3]} training recipes. Instead, the student model is trained on mixtures with the same number of sources for the bootstrapped mixtures where the teacher model had performed adequately. Unlike previous teacher-student methods which use the same teacher-estimated source-pairs as the targets for the student network [4]}, [5]}, the proposed bootstrapped mixtures increase the input mixture diversity and allow faster model training. This is especially useful in settings with a large distribution shift between teacher's and student's training data. Moreover, in contrast to the self-training approach in [6]}, RemixIT employs a lifelong learning process instead of freezing the teacher, producing a static self-labeled dataset offline and then train the student model in isolation. Our method is general enough that could be paired with any online co-training method which continuously updates the teacher's weights in addition to the main student training.
